Rescheduling Decisions Research Articles

Analysis of Roller in V-Belt Base Cutting for Material Optimization

Abstract: Along with the fourth industrial revolution different tools coming from optimization are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under certainty, it is complicated task to decide whether rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a trade-off between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates optimization algorithms. To prove the effectiveness, we first model work scheduling problem with dependent setup and limited resources inspired by an industrial application. Then we solve the scheduling problem through a hybrid approach and train the model for rescheduling patterns. Finally, we compare its rescheduling performance with periodical approaches. Through observing the simultaneous results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and delays. The main contribution of the work is the formalization of the problem, the development, solution methods and the optimization-based framework for supporting rescheduling decisions. Keywords: Optimization, Analysis, Production, Framework, Simulation.

Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services

Rapid printed circuit boards (PCB) prototyping refers to the pilot production of PCB before mass production for verifying the circuit design. The rapid PCB prototyping service providers are pursuing the mass individualization paradigm. Yet, these providers suffer from low efficiency and high cost in the large-scale small-batched mixed pilot production of rapid PCB prototyping. To improve the production efficiency in mass individualization of rapid PCB prototyping, this paper proposed a cloud-edge orchestration-based bi-level autonomous process control (CEO-BAPC) framework. A blockchained smart contracts-driven multi-agent system is established at the edge to realize efficient tasks coordination under disturbances. Based on the operation events data and control decisions collected at the edge, a customized deep learning-driven prediction model is established at the cloud for supporting the rescheduling decisions. The blockchained smart contracts (e.g., Stackelberg game solution) at the edge proactively decentralize short-term fine-grained individualized task execution among manufacturing units/machines and make the results available on upper-level deep learning model at the cloud for supporting holistic rescheduling decisions. The bi-level autonomous process control architecture avoids the inconsistency between holistic control and local execution under frequent random disturbances and thereby realizes efficient manufacturing of mass individualization. Through a simulated case based on data collected from a rapid PCB prototyping service provider in China, the feasibility and efficiency of the proposed CEO-BAPC framework are verified.

A Mixed-integer Linear Program for Real-time Train Platforming Management

Unexpected events may perturb operations and generate conflicts that must be addressed promptly to limit delay propagation and other negative impacts on the network. The real-time railway traffic management problem deals with disruptions in railway networks, including tracks, junctions and stations. When they happen in station areas, new decisions involving train platforming, rerouting, ordering and timing must be made in real time. This paper explores a mesoscopic approach to deal with disruptions at rail stations. A mathematical programming-based model is proposed to determine re-routing and re-scheduling decisions for railway traffic in a station area. The key steps of the approach, which simulate what happens in real-time traffic management, are: i) an initial off-line preprocessing stage of the set of feasible routes originally planned, ii) a second preprocessing stage which analyses the disruption and sets the necessary parameters for the last step iii), which consists of an integer programming model that seeks solutions which minimise deviations from planned train schedules and assigns new and appropriate platforms (if necessary). Computational experiments show that realistic instances can be solved near to optimality using CPLEX in very short times. This allows to consider this methodology for solving real time traffic management problems.

End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning

With the advent of the socio-technical manufacturing paradigm, the way in which rescheduling decisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly dynamic conditions. To cope with uncertain production environments, a drastic increase in the type and degree of automation used at the shop floor for handling unforeseen events and unplanned disturbances is required. In this work, the on-line rescheduling task is modelled as a closed-loop control problem in which an artificial autonomous agent implements a control policy generated off-line using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control policy is stored in a deep neural network, which is used to select repair actions in order to achieve a small set of repaired goal states. The rescheduling agent is trained using Proximal Policy Optimisation based on a wide variety of simulated transitions between schedule states using colour-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end deep learning of successful rescheduling policies to encode task-specific control knowledge that can be understood by human experts.

Automated job shop scheduling with dynamic processing times and due dates using project management and industry 4.0

ABSTRACT This paper presents an automated model for improving job shop scheduling by incorporating Industry 4.0 and project management. The proposed model develops dynamic and adaptive schedules to incorporate real-time information about processing times (including random unexpected events) and due dates, reflecting the impact of industry 4.0 on rescheduling decisions. The model minimizes the earliness and tardiness costs while considering the rescheduling costs and is motivated by the real-life case study from a local company. This study applied Earned Value (EV) and Forecasted Total Cost at Completion () concepts and integrated it with mixed integer linear programming (MILP) model to design an adaptive automated scheduling system. The paper presents a new application of project management concept in MILP job shop scheduling. Also, this research proposes new rescheduling concept to minimize unnecessary schedule changes while providing the best possible schedule to process all the jobs.

Integrated Timetable Rescheduling for Multidispatching Sections of High-Speed Railways During Large-Scale Disruptions

Under the condition of network operation of high-speed railways (HSRs), the influence of disruptions on the train control and dispatching at the current line and related lines is more and more significant. This article focuses on the timetable cooperative rescheduling problem with multidispatching sections of HSRs from a macroscopic perspective in the case of large disruptions. The problem is formulated as a mixed-integer linear program (MILP) model on the objectives of minimizing the weighted sum of the arrival delay time of trains, the delay time of depart trains at the handover station, and the number of delays of trains at all stations. The strategies of retiming and reordering are adopted to generate the rescheduling scheme and reduce delay propagation by making full use of three kinds of buffer time reserved in the timetable, i.e., buffer times of train operation in the station, train running in the section, and electric multiple unit (EMU) connection. A case study of the timetable rescheduling at the two adjacent dispatching sections of the Beijing–Shanghai HSRs line is conducted to evaluate the performance of the proposed integrated rescheduling approach. The relationship between computing time and the quality of rescheduling schemes is also investigated. The computational results show that the proposed approach can generate a conflict-free timetable with the minimum arrival delay time and the number of delays of all trains at all stations compared to the nonintegrated rescheduling approach and the benchmark solution of the first-come-first-serve (FCFS) approach. The propagation of delay between dispatching sections is also greatly suppressed. The results can provide support for dispatchers to making reasonable rescheduling decisions in the case of large disruptions.

Machine learning and optimization for production rescheduling in Industry 4.0

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

Will it really happen? Disambiguating of the hypothetical and real “Next Wednesday's meeting” question in Mandarin speakers

Previous research has consistently shown that Mandarin speakers tended to adopt the Moving Time perspective when addressing the “Next Wednesday's meeting” question. Using the revised paradigm, Experiment 1 found that Mandarin speakers showed no preference for either the ego-moving perspective or the time-moving perspective when reading the single verb “移动(move)” which lacks a precise spatial meaning, suggesting that the temporal ambiguity of the probe is rooted in the directional neutrality of the verb. Experiment 2 proceeded to use this ambiguous phrase to investigate whether Mandarin speakers’ responses to the hypothetical probe are predicative of the rescheduling decisions they would make in real life. The results indicate that people's temporal reasoning in real life contexts can dramatically diverge from their thinking in hypothetical scenarios because people tend to put off the meeting until a later time to ease the inconvenience in real rescheduling management practice. Furthermore, the correlations between temporal perspective preference and individual-differences measures were only found for the hypothetical-condition samples. However, by providing real contextual information available to participants when responding to a hypothetical question—thereby reducing the need for mental simulation—we brought people's responses into concordance with their answers in real situations (Experiment 3). This is the first experimental demonstration that hypothetical disambiguation studies, while a valuable contribution to our understanding of time cognition, fail to capture the most salient motivational force shaping actual temporal thinking because of the decontextualized nature.

Commentary on Cairns et al. (2020): Codeine rescheduling-where to from here?

Commentary on Cairns et al. (2020): Codeine rescheduling-where to from here?

Adapting GA to solve a novel model for operating room scheduling problem with endogenous uncertainty

This paper addresses a new variant of the daily operating room scheduling problem in which, surgeries have stochastic durations, and in order to get a more flexible schedule, the initial scheduling and the rescheduling decisions are simultaneously considered within a single optimization model. The main point in the formulation of this problem is that the time of the uncertainty realization is decision dependent and hence, the uncertainty is of endogenous nature which is a new topic in stochastic programming (SP) literature. First, a novel mathematical model is developed for this problem, and an illustrative example is provided to justify the importance of the joint optimization of scheduling and rescheduling decisions. Then, the structure of the proposed model is utilized to develop a genetic algorithm (GA) to solve large instances of this NP-hard optimization problem. Computational experiments on some randomly generated test problems, confirm the efficiency of the proposed GA in terms of the solution quality and time. Moreover, the results indicate that a cost reduction may be achieved by integrating scheduling and rescheduling decisions.

Due time driven surgery scheduling

In many hospitals there are patients who receive surgery later than what is medically indicated. In one of Europe's largest hospitals, the University Hospital Leuven, this is the case for approximately every third patient. Serving patients late cannot always be avoided as a highly utilized OR department will sometimes suffer capacity shortage, occasionally leading to unavoidable delays in patient care. Nevertheless, serving patients late is a problem as it exposes them to an increased health risk and hence should be avoided whenever possible. In order to improve the current situation, the delay in patient scheduling had to be quantified and the responsible mechanism, the scheduling process, had to be better understood. Drawing from this understanding, we implemented and tested realistic patient scheduling methods in a discrete event simulation model. We found that it is important to model non-elective arrivals and to include elective rescheduling decisions made on surgery day itself. Rescheduling ensures that OR related performance measures, such as overtime, will only loosely depend on the chosen patient scheduling method. We also found that capacity considerations should guide actions performed before the surgery day such as patient scheduling and patient replanning. This is the case as those scheduling strategies that ensure that OR capacity is efficiently used will also result in a high number of patients served within their medically indicated time limit. An efficient use of OR capacity can be achieved, for instance, by serving patients first come, first served. As applying first come, first served might not always be possible in a real setting, we found it is important to allow for patient replanning.

Dispatching and coordination in multi-area railway traffic management

This paper deals with the development of decision support systems for traffic management of large and busy railway networks in case of severe disturbances. Railway operators typically structure the control of complicated networks into the coordinated control of several local dispatching areas. A dispatcher takes rescheduling decisions on the trains running on its local area while a coordinator addresses global issues that may arise between areas. While several advanced train dispatching models and algorithms have been proposed to support the dispatchers' task, the coordination problem did not receive much attention in the literature on train scheduling. This paper presents new heuristic algorithms for both local dispatching and coordination and compares centralized and distributed procedures to support the task of dispatchers and coordinators. We adopt dispatching procedures driven by optimization algorithms and based on local or global information and decisions. Computational experiments on a Dutch railway network, actually controlled by ten dispatchers, assess the performance of the centralized and distributed procedures. Various traffic disturbances, including entrance delays and blocked tracks, are analyzed on various time horizons of traffic prediction. Results show that the new heuristics clearly improve the global performance of the network with respect to the state of the art.

Rescheduling nursing shifts: scoping the challenge and examining the potential of mathematical model based tools

To review research in the literature on nursing shift scheduling / rescheduling, and to report key issues identified in a consultation exercise with managers in four English National Health Service trusts to inform the development of mathematical tools for rescheduling decision-making. Shift rescheduling is unrecognised as an everyday time-consuming management task with different imperatives from scheduling. Poor rescheduling decisions can have quality, cost and morale implications. A systematic critical literature review identified rescheduling issues and existing mathematic modelling tools. A consultation exercise with nursing managers examined the complex challenges associated with rescheduling. Minimal research exists on rescheduling compared with scheduling. Poor rescheduling can result in greater disruption to planned nursing shifts and may impact negatively on the quality and cost of patient care, and nurse morale and retention. Very little research examines management challenges or mathematical modelling for rescheduling. Shift rescheduling is a complex and frequent management activity that is more challenging than scheduling. Mathematical modelling may have potential as a tool to support managers to minimise rescheduling disruption. The lack of specific methodological support for rescheduling that takes into account its complexity, increases the likelihood of harm for patients and stress for nursing staff and managers.

A heterogeneous latent class model of activity rescheduling, route choice and information acquisition decisions under multiple uncertain events

Travel information plays a central role in reducing uncertainty and persuading travelers to act in particular ways. It may result in activity-travel rescheduling decisions. The modeling of such behavior is relatively complex as it goes beyond simple route/link choice. Moreover, it involves multiple uncertain events that may appear at different points in the future. In addition, travelers may differ in their attitudes toward risk and the decision heuristics they apply. This paper develops a decision model that incorporates these aspects and reports the results of an interactive computer experiment which was developed to collect data on activity-travel rescheduling under multiple uncertain events and information acquisition. The results of the model estimation suggest that the model performed well.

Optimal inter-area coordination of train rescheduling decisions

Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers’ work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management. This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations.

Optimal inter-area coordination of train rescheduling decisions

Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers’ work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management.This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations.

3401 サービスモデリングによるフライトのリスケジューリング手法の提案とシステム構築(OS12-4 ライフサイクル設計とサービス工学IV,OS12 ライフサイクル設計とサービス工学)

It is a well-known fact that punctual flights of airlines result in high customer satisfaction. However, the flights are frequently delayed or canceled due to unexpected incidents. These incidents are to be dealt by rescheduling, which intends to maintain a high level of customer satisfaction while adjusting resources involved. Rescheduling is a highly complex problem, because of large number of resources involved and existence of some candidates of possible plan. Today, the flights are rescheduled by operator's implicit knowledge which is cultivated from his/her long experience. For these reasons, a computational support method of rescheduling of flight service is strongly desired to support the difficult rescheduling decisions. This paper proposes a decision support method of flight rescheduling. In order to visualize an analyze the system of flight service, this paper represents flight services as a service model consists of three sub service models: (1) transportation service, (2) operation service, and (3) maintenance service. This paper also proposes an algorithm of the rescheduling based on the flight service model, and evaluation method of flight plans to support operator's decision making. Proposed service model and evaluation method is applied to actual data of flight services as a case study. The result shows practical effectiveness of propositions.

A multi-instrumented approach to observing the activity rescheduling decision process

Activity scheduling simulation models represent an emerging and proposing approach to forecasting travel demand. The most significant developmental challenge is the lack of empirical data on how people actually proceed through the scheduling and conflict resolution process. This paper develops a new methodology to collect data about the rescheduling decision process. The data collection involves six stages: preplanned schedule interview, coding of the preplanned schedule, second-by-second Global Positioning System tracking, internet-based prompted recall diary, detection of rescheduling decisions (via comparison of planned versus executed activities), and a final in-depth interview probing the how and why of rescheduling decisions. Each stage of the methodology is described in detail with example results drawn from a pilot study. Key discoveries include: elicitation of multiple preplanned schedule reporting methods (verbal, point-form, calendar); discovery that activity attributes (time, location, involved persons) are planned on significantly different time horizons and include partial elaboration; and provision of new insights into how and why rescheduling decisions are made. A method for automatically tracking rescheduling decisions was also discovered. Overall, the new methodology has potential to contribute to the development of more realistic models of the entire scheduling process, especially rescheduling and conflict resolution sub-models.

A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions

In this paper, a new fuzzy logic based decision support system for parallel machine scheduling/rescheduling in the presence of uncertain disruptions is presented. It is applied to a real-life problem identified in a pottery company. The uncertain disruption considered is glaze shortage, defined by two parameters: number of glaze shortage occurrences and glaze shortage duration. Both parameters are specified imprecisely. They are modelled and combined using standard fuzzy sets and level 2 fuzzy sets, respectively. In order to deal with the glaze shortage disruption, a predictive–reactive scheduling approach is proposed and implemented. It is defined as a two-step procedure. In the first step, a predictive schedule is generated in such a way as to being capable of absorbing the impact of the glaze shortage disruption. In the second step, rescheduling is applied when the impact of the glaze shortage disruption is too high. Two sets of Sugeno type rules are proposed to support rescheduling decision making. One set of the fuzzy rules determines when to reschedule, whilst the other one determines which rescheduling method to use. Various tests are carried out that show that (1) the predictive schedules have good performance in the presence of uncertain disruptions and (2) the fuzzy inference generates appropriate rescheduling decisions.

Modeling the Impact of Travel Information on Activity-Travel Rescheduling Decisions Under Conditions of Travel Time Uncertainty

This study elaborates on a model system developed in earlier papers to predict the perceived value and use of travel information. The value of travel information is conceptualized as the extent to which the information allows the individual to make better activity-travel scheduling decisions at the beginning of the day and during execution of the schedule. By taking the broader schedule context into account, the model is sensitive to the impact of information and decisions on the full activity-travel pattern. Furthermore, the model includes Bayesian mechanisms to make sure that beliefs about travel times, and other uncertain events, and the credibility of the information source are updated each time information is received and the real travel time is experienced. This paper describes the results of numerical simulations conducted to illustrate the system and to derive theoretical implications from the model. The simulations show that the schedule context, learning, and expected information gain in combina...